Synchronization Control for a Class of Discrete-Time Dynamical Networks With Packet Dropouts: A Coding–Decoding-Based Approach

Liu

et al. 2019

Self Cite

144

Summary This paper is concerned with the distributed filtering problem for a class of nonlinear time‐delay system over sensor networks subject to multiplicative link noises and switching topology. Both discrete and distributed time delays are included in the system model. Each sensor estimates the system state by means of the measurements not only from itself but also from its neighboring nodes according to an interactive topology. The multiplicative stochastic link noises are taken into consideration to reflect the random perturbations during the information exchanges between sensor nodes. The considered communication topology is switching according to certain predetermined rules. The purpose of the addressed problem is to develop a distributed filtering strategy such that, in the presence of multiplicative stochastic link noises and switching topology, the resulting filtering error dynamics is exponentially stable in the mean square sense and also satisfies the prespecified weighted disturbance attenuation level. In light of the average dwell time technique in combination with stochastic analysis, sufficient conditions are derived for the solvability of the addressed distributed filtering problem, and the desired filtering gains are then obtained through solving certain convex optimization problems. An illustrative simulation example is presented to demonstrate the correctness and applicability of the obtained theoretical results.

Section: Resultsmentioning

confidence: 99%

“…One of the future research topics would be to extend the main results to more complicated systems with more performance requirements. 9,[43][44][45][46]…”

Section: Resultsmentioning

confidence: 99%

Distributed filtering for nonlinear time‐delay systems over sensor networks subject to multiplicative link noises and switching topology

Liu

et al. 2019

Self Cite

144

“…Finally, a simulation example has also been given to verify the effectiveness of the proposed filter algorithm. Some future research directions will be focused on the extension of the established approach to switched systems, nonlinear systems, complex/neural networks, and the application of the obtained results on fault/spammer detection …”

Section: Discussionmentioning

confidence: 99%

Local design of distributed H_∞‐consensus filtering over sensor networks under multiplicative noises and deception attacks

Han

Dong

et al. 2019

Self Cite

Summary This paper is concerned with the local design of the distributed H∞‐consensus filtering problem for a class of discrete time‐varying systems subject to both multiplicative noises and deception attacks over sensor networks. The target plant and the measuring sensors are disturbed by multiplicative noises with known statistics. The malicious signal involved in deception attacks is constrained by a specific sector‐like bounded condition, which reflects certain tolerable bound on the difference between the attack signal and the true signal. Attention is paid to the design of filter gains for guaranteeing a desirable filtering performance that simultaneously characterizes the filtering accuracy and the consensus requirement. To handle the proposed filtering problem, the supply rate function is firstly chosen for each node and then the dissipation matrix is constructed as a column substochastic matrix based on the stochastic vector dissipation theory. Subsequently, sufficient conditions by means of recursive linear matrix inequalities are presented for each node such that the filtering error and the consensus error satisfy the desirable H∞‐consensus performance index over a finite horizon. Finally, an illustrative simulation is presented to demonstrate the effectiveness of the proposed filter strategy.

“…[1][2][3][4] A number of filtering methods have been proposed to provide the desired state estimates according to various performance requirements. [5][6][7][8][9][10] For example, the well-known Kalman filter (or extended Kalman filter) has been widely applied to linear (or nonlinear) systems with Gaussian noises. 2 The H ∞ filtering method is capable of ensuring a prespecified disturbance attenuation level on the filtering error subject to energy bounded noises.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic‐constrained filtering for a class of nonlinear systems with improved static event‐triggered communication

Tian

Zou

et al. 2018

Self Cite

172

This paper is concerned with the probabilistic-constrained filtering problem for a class of time-varying systems with stochastic nonlinearities and state constraints. An improved static event-triggering scheme is used to reduce unnecessary signal transmissions on the communication channel, where a time-varying triggering parameter is designed according to engineering practice. The aim of the problem addressed is to design a time-varying filter such that (1) the prescribed probabilistic constraints on the estimation error are satisfied (ie, the probability for the estimation error to be confined to the given ellipsoidal set is larger than a prescribed value) and (2) the ellipsoid is minimized at each time instant in the sense of the matrix norm. First, the probabilistic constraints are handled by means of the multidimensional Chebyshev bounds. By using recursive matrix inequalities, stochastic analysis is conducted to establish sufficient conditions for the existence of the desired probabilistic-constrained filter. Then, a recursive optimization algorithm is proposed to design the filter gain matrices. Finally, a simulation example is proposed to demonstrate the effectiveness and applicability of the proposed method. KEYWORDS event-triggering scheme, filter design, probabilistic constraints, stochastic nonlinearity, time-varying systems 1484